# Design of a Supervisory Control System for Autonomous Operation of Advanced Reactors

arXiv: https://arxiv.org/abs/2209.04334 · HTML: https://mlsanity.com/paper/2209.04334/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Dynamic mode decomposition with control](https://mlsanity.com/paper/1409.6358/) (2014)
- [Numerical Demonstration of Multiple Actuator Constraint Enforcement Algorithm for a Molten Salt Loop](https://mlsanity.com/paper/2202.02094/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants](https://mlsanity.com/paper/2401.13020/) (2024)
- [A Digital Twin Framework for Generation-IV Reactors with Reinforcement Learning-Enabled Health-Aware Supervisory Control](https://mlsanity.com/paper/2506.17258/) (2025)
- [Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability](https://mlsanity.com/paper/2604.07292/) (2026)

## Abstract (arXiv)

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the control system. The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system's operating conditions, and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. To accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured, the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40 % power drop transient. Adaptability was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was demonstrated by enforcing constraints under noisy environments.